Diabetic RetinopathyNCT04723160
Computer Aided Diagnosis of Multiple Eye Fundus Diseases From Color Fundus Photograph
An observational study of Diabetic Retinopathy and Retinal Vein Occlusion, run by Visionary Intelligence Ltd. at 5 sites in China.
Trial summary
Blindness can be caused by many ocular diseases, such as diabetic retinopathy, retinal vein occlusion, age-related macular degeneration, pathologic myopia and glaucoma. Without timely diagnosis and adequate medical intervention, the visual impairment can become a great burden on individuals as well as the society. Read more about this study
- Who is it for?
- Ages 18 to 75 yearsHealthy volunteers can take part
- Conditions the study is for
- Diabetic Retinopathy, Retinal Vein Occlusion, Retinal Artery Occlusion
- What is being tested?
- Software assisted imaging diagnosis
- How many people
- 748 participants
- Where
- 5 sitesChina
- Study dates
- 10 Aug 2020to 30 May 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 75 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Any patients who meet the eligibility criteria.
Who may be able to join
- Age between 18 and 75.
- Anyone need to take fundus photograph in clinical.
- Understand the study and volunteer to sign the informed consent.
- For fundus images of participants, the optic disc, fovea, the upper and lower vessel bow should be included in the fundus field.
Who cannot join
- Participants has any eye that cannot take fundus photos.
- Participants have joined or is participating in other clinical trial within one month.
- Participants who have any other issue that cannot be enrolled.
- Participants with cloudy refractive media that cannot take fundus photos or get clouding fundus photos.
- Participants with low quality fundus photos like incompetent vision field, overexposed/underexposed, out of focus, too many shadow or dirties and so on.
Trial locations and contacts
5 sites in 1 country.
5 sites in 1 country
China5 sites
Peking Union Medical College Hospital, Chinese Academy of Medical Sciences
Beijing, Beijing Municipality, 100730
West China Hospital of Sichuan University
Chengdu, Sichuan
The Second Hospital of Hebei Medical University
Shijiazhuang, Hebei
Tianjin Medical University Eye Hospital
Tianjin, Tianjin Municipality
Eye Hospital, WMU Zhejiang Eye Hospital
Wenzhou, Zhejiang
About this study
Official title: A Prospective, Multicenter, Blinded Reading, Self Controlled, Superiority Priority Clinical Trial of Assisted Fundus Image Diagnosis Software for the Diagnosis of Multiple Eye Fundus Diseases
Blindness can be caused by many ocular diseases, such as diabetic retinopathy, retinal vein occlusion, age-related macular degeneration, pathologic myopia and glaucoma. Without timely diagnosis and adequate medical intervention, the visual impairment can become a great burden on individuals as well as the society. It is estimated that China has 110 million patients under the attack of diabetes, 180 million patients with hypertension, 120 million patients suffering from high myopia and 200 million people over 60 years old, which suggest a huge population at the risk of blindness. Despite of this crisis in public health, our society has no more than 3,000 ophthalmologists majoring in fundus oculi disease currently. As most of them assembling in metropolitan cities, health system in this field is frail in primary hospitals. Owing to this unreasonable distribution of medical resources, providing medical service to hundreds of millions of potential patients threatened with blindness is almost impossible.
To solve this problem, this software (MCS) was developed as a computer-aided diagnosis to help junior ophthalmologists to detect 13 major retina diseases from color fundus photographs. This study has been designed to validate the safety and efficiency of this device.
Read the detailed description
As a prospective clinical trial, This study enjoys multicentric, blind film reading, self-control and superiority test design. In total, 1,500 retinal fundus images from 750 individuals in need of fundus examination (one image for every single eye) were selected. Then a test group, along with a control group was set up in our study. For the test group, ophthalmologists read images with the aid of the assistant software(MCS). In contrast, the same work in the control group was finished by ophthalmologists independently. Meanwhile, the gold standard were obtained from the cooperation of senior ophthalmologists. Diagnoses of both groups were compared with those of the gold standard, thus the investigators could evaluated the safety and effectiveness of this assistant software in diagnosis.
The primary endpoint of this study is the superiority of the consistency rate of the test group. A diagnosis for an image is consistent if it gives the same negative result as the reference standard, or reveals any one condition indicated by the reference standard. The consistency rate is the rate of consistent diagnoses for all the involved images. One control group is designed, where each doctor reads and diagnoses, and give at most 3 possible conditions for each image. In the test group, doctors do the same thing with the help of this software. The investigators in the test group and control group are the same and they are chosen from ophthalmologists with 1~3 years experience. The reference standard of each fundus image is collaboratively given by retinal specialists/fellows from 5 centers. The investigator of XieHe center is the arbitrator if full consensus cannot be reached for any image during the building of reference standard.
- Conditions
- Diabetic Retinopathy, Retinal Vein Occlusion, Retinal Artery Occlusion, Central Serous Chorioretinopathy, Pathologic Myopia, Retinitis Pigmentosa
- Being tested
- Software assisted imaging diagnosis
- Keywords
- multiple eye fundus diseases
- Medical subject headings
- Diabetic Retinopathy, Retinal Vein Occlusion, Retinal Artery Occlusion, Central Serous Chorioretinopathy, Retinitis Pigmentosa, Epiretinal Membrane, Retinal Perforations, Ocular Hypertension
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Software assisted imaging diagnosisDiagnostic test
In the test group, diagnoses are given with the help of the software.
| Study group | Role | What they receive |
|---|---|---|
| Test Group | ophthalmologists read images applying the assistant software Receives: Diagnostic Test: Software assisted imaging diagnosis | |
| Control Group | ophthalmologists read images independently |
What is being measured
The primary outcome is the main question the study is designed to answer.
consistent rate of diagnoses
Formula for calculation: consistent rate of diagnoses=number of images with consistent diagnosis/ total number of images × 100%.
Method: the diagnoses from the test group and the control group were compared with diagnoses from the gold standard. For each image, if one or more diagnoses were consistent with those of the gold standard, which means at least one label existed in the intersection of diagnoses from the test group(or the control group)and those from the gold standard, it would be classified as "image with consistent diagnosis". Otherwise, it would be classified as "image without consistent diagnosis". After above-mentioned steps, the investigators had obtained the number of images with consistent diagnosis in each group. As images with 1-2 labels account for the majority in actual work, the investigators stipulated that each image in both groups could be marked with 3 labels at most in case of invalid improvement in consistent rate owing to multiple selections.
Measured: through study completion, an average of 1 year
Secondary outcomes (3)
sensitivity and specificity of software's diagnoses for each diseases
sensitivity and specificity of software's diagnoses for each diseases
Measured: through study completion, an average of 1 year
PPV and NPV of software's diagnoses for each diseases
PPV(Positive Predictive Value) and NPV(Negative Predictive Value) of software's diagnoses for each diseases
Measured: through study completion, an average of 1 year
full coincidence rate of software's diagnoses
The full consistency rate is the rate of fully consistent diagnoses in the set. A diagnosis is fully consistent it is exactly the same as the reference standard.
Measured: through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Jan 2021
- Study start10 Aug 2020
- Primary completion10 Mar 2021
- Study completion30 May 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Diabetic RetinopathyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 748 participants (actual)
Sponsor and investigators
- Lead sponsor
- Visionary Intelligence Ltd.
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- Peking University First Hospital, Beijing Municipal Science & Technology Commission
- Responsible party
- Sponsor
- Investigators
- You xin Chen, PHD, Study chair, Peking Union Medical College
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04723160
- Sponsor's protocol ID
- BLG-20200101
- Status last verified
- Dec 2021
- First submitted
- 17 Jan 2021
- Last updated
- 30 Dec 2021
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Li B, Chen H, Yu W, Zhang M, Lu F, Ma J, Hao Y, Li X, Hu B, Shen L, Mao J, He X, Wang H, Ding D, Li X, Chen Y. The performance of a deep learning system in assisting junior ophthalmologists in diagnosing 13 major fundus diseases: a prospective multi-center clinical trial. NPJ Digit Med. 2024 Jan 11;7(1):8. doi: 10.1038/s41746-023-00991-9.
PubMed 38212607
Study at a glance
From the registry record, last updated by the sponsor on 30 Dec 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Prospective, Multicenter, Blinded Reading, Self Controlled, Superiority Priority Clinical Trial of Assisted Fundus Image Diagnosis Software for the Diagnosis of Multiple Eye Fundus Diseases
- ClinicalTrials.gov ID
- NCT04723160
- Acronym / sponsor ID
- BLG-20200101
- Conditions
- Diabetic Retinopathy, Retinal Vein Occlusion, Retinal Artery Occlusion, Central Serous Chorioretinopathy, Pathologic Myopia, Retinitis Pigmentosa
- Keywords
- multiple eye fundus diseases
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 748 participants (actual)
- Lead sponsor
- Visionary Intelligence Ltd.Industry (pharma, biotech, devices)
- Collaborators
- Peking University First Hospital, Beijing Municipal Science & Technology Commission
- Study start
- 10 Aug 2020 (actual)
- Primary completion
- 10 Mar 2021 (actual)
- Study completion
- 30 May 2021 (actual)
- First posted
- 25 Jan 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Blindness can be caused by many ocular diseases, such as diabetic retinopathy, retinal vein occlusion, age-related macular degeneration, pathologic myopia and glaucoma. Without timely diagnosis and adequate medical intervention, the visual impairment can become a great burden on individuals as well as the society. It is estimated that China has 110 million patients under the attack of diabetes, 180 million patients with hypertension, 120 million patients suffering from high myopia and 200 million people over 60 years old, which suggest a huge population at the risk of blindness. Despite of this crisis in public health, our society has no more than 3,000 ophthalmologists majoring in fundus oculi disease currently. As most of them assembling in metropolitan cities, health system in this field is frail in primary hospitals. Owing to this unreasonable distribution of medical resources, providing medical service to hundreds of millions of potential patients threatened with blindness is almost impossible.
To solve this problem, this software (MCS) was developed as a computer-aided diagnosis to help junior ophthalmologists to detect 13 major retina diseases from color fundus photographs. This study has been designed to validate the safety and efficiency of this device.
Detailed description
As a prospective clinical trial, This study enjoys multicentric, blind film reading, self-control and superiority test design. In total, 1,500 retinal fundus images from 750 individuals in need of fundus examination (one image for every single eye) were selected. Then a test group, along with a control group was set up in our study. For the test group, ophthalmologists read images with the aid of the assistant software(MCS). In contrast, the same work in the control group was finished by ophthalmologists independently. Meanwhile, the gold standard were obtained from the cooperation of senior ophthalmologists. Diagnoses of both groups were compared with those of the gold standard, thus the investigators could evaluated the safety and effectiveness of this assistant software in diagnosis.
The primary endpoint of this study is the superiority of the consistency rate of the test group. A diagnosis for an image is consistent if it gives the same negative result as the reference standard, or reveals any one condition indicated by the reference standard. The consistency rate is the rate of consistent diagnoses for all the involved images. One control group is designed, where each doctor reads and diagnoses, and give at most 3 possible conditions for each image. In the test group, doctors do the same thing with the help of this software. The investigators in the test group and control group are the same and they are chosen from ophthalmologists with 1~3 years experience. The reference standard of each fundus image is collaboratively given by retinal specialists/fellows from 5 centers. The investigator of XieHe center is the arbitrator if full consensus cannot be reached for any image during the building of reference standard.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Test Group | – | Diagnostic Test: Software assisted imaging diagnosisophthalmologists read images applying the assistant software |
| Control Group | – | –ophthalmologists read images independently |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Any patients who meet the eligibility criteria.
Key inclusion criteria (4 listed)
- Age between 18 and 75.
- Anyone need to take fundus photograph in clinical.
- Understand the study and volunteer to sign the informed consent.
- For fundus images of participants, the optic disc, fovea, the upper and lower vessel bow should be included in the fundus field.
Key exclusion criteria (5 listed)
- Participants has any eye that cannot take fundus photos.
- Participants have joined or is participating in other clinical trial within one month.
- Participants who have any other issue that cannot be enrolled.
- Participants with cloudy refractive media that cannot take fundus photos or get clouding fundus photos.
- Participants with low quality fundus photos like incompetent vision field, overexposed/underexposed, out of focus, too many shadow or dirties and so on.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Software assisted imaging diagnosisIn the test group, diagnoses are given with the help of the software. | Diagnostic test | Test Group |
Primary outcome
consistent rate of diagnoses
Time frame: through study completion, an average of 1 year
Formula for calculation: consistent rate of diagnoses=number of images with consistent diagnosis/ total number of images × 100%. Method: the diagnoses from the test group and the control group were compared with diagnoses from the gold standard. For each image, if one or more diagnoses were consistent with those of the gold standard, which means at least one label existed in the intersection of diagnoses from the test group(or the control group)and those from the gold standard, it would be classified as "image with consistent diagnosis". Otherwise, it would be classified as "image without consistent diagnosis". After above-mentioned steps, the investigators had obtained the number of images with consistent diagnosis in each group. As images with 1-2 labels account for the majority in actual work, the investigators stipulated that each image in both groups could be marked with 3 labels at most in case of invalid improvement in consistent rate owing to multiple selections.
3 secondary outcomes
sensitivity and specificity of software's diagnoses for each diseases
Time frame: through study completion, an average of 1 year
PPV and NPV of software's diagnoses for each diseases
Time frame: through study completion, an average of 1 year
full coincidence rate of software's diagnoses
Time frame: through study completion, an average of 1 year
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Dec 2021.
- Derived from this study: PubMed 38212607Li B, Chen H, Yu W, Zhang M, Lu F, Ma J, Hao Y, Li X, Hu B, Shen L, Mao J, He X, Wang H, Ding D, Li X, Chen Y. The performance of a deep learning system in assisting junior ophthalmologists in diagnosing 13 major fundus diseases: a prospective multi-center clinical trial. NPJ Digit Med. 2024 Jan 11;7(1):8. doi: 10.1038/s41746-023-00991-9.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
You xin Chen, PHD study chair
Peking Union Medical College
5 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Diabetic Retinopathy and Retinal Vein Occlusion, run by Visionary Intelligence Ltd. at 5 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 30 Dec 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
consistent rate of diagnoses (time frame: through study completion, an average of 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04723160, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Dec 2021.